Automatic Speaker Recognition as a Measurement of Voice Imitation and Conversion

Abstract

Voices can be deliberately disguised by means of human imitation or voice conversion. The question arises as to what extent they can be modified by using either of both methods. In the current paper, a set of speaker identification experiments are conducted; first, analysing some prosodic features extracted from voices of professional impersonators attempting to mimic a target voice and, second, using both intragender and crossgender converted voices in a spectral-based speaker recognition system. The results obtained in the current experiments show that the identification error rate increases when testing with imitated voices, as well as when using converted voices, especially the crossgender ones.